Inverse Design of Broadband Dispersion Compensation Fiber Based on Deep Learning and Differential Evolution Algorithm
A Ge-doped dual-core dispersion compensation photonic crystal fiber (DC-DCPCF) is proposed. The small diameters of two layers’ air holes make DC-DCPCF form a dual-core structure, which is conducive to broadband dispersion compensation. Low Ge-doped silica as the only background material r...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/10136190/ |
| _version_ | 1797808514836463616 |
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| author | Fuxiao Ma Yunjie Ma Peili Li Weihua Shi |
| author_facet | Fuxiao Ma Yunjie Ma Peili Li Weihua Shi |
| author_sort | Fuxiao Ma |
| collection | DOAJ |
| description | A Ge-doped dual-core dispersion compensation photonic crystal fiber (DC-DCPCF) is proposed. The small diameters of two layers’ air holes make DC-DCPCF form a dual-core structure, which is conducive to broadband dispersion compensation. Low Ge-doped silica as the only background material reduces the preparation difficulty and cost. It is inversely designed by using artificial neural network (ANN) combined with differential evolution algorithm (DE) to obtain target dispersion compensation. ANN replaces the finite element method to accomplish fast forward prediction of DC-DCPCF properties. DE solves the single solution problem of single or cascade network that makes it flexible and reproducible. The results demonstrate that the designed DC-DCPCF can not only compensate 45 and 25 times its length of Corning single-mode fiber 28 (SMF28) in S+C+L+U bands and E+S+C+L+U bands respectively, but also accurately compensate the residual dispersion with effective dispersion compensation being only +0.005∼+0.842ps/(nm·km) and −0.03∼+1.31ps/(nm·km), respectively. In addition, the kappa values of DCP-PCF are well matched with SMF28 in the broadband wavelength range. It takes only about 10 seconds to complete the inverse design of the target DC-DCPCF. It provides a design method for custom DC-DCPCF and an efficient inverse design solution for photonic automation in fiber optical communication systems. |
| first_indexed | 2024-03-13T06:39:41Z |
| format | Article |
| id | doaj.art-ff60e3e33db745afb87db4c89de22e5b |
| institution | Directory Open Access Journal |
| issn | 1943-0655 |
| language | English |
| last_indexed | 2024-03-13T06:39:41Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj.art-ff60e3e33db745afb87db4c89de22e5b2023-06-08T23:00:22ZengIEEEIEEE Photonics Journal1943-06552023-01-011531710.1109/JPHOT.2023.327712910136190Inverse Design of Broadband Dispersion Compensation Fiber Based on Deep Learning and Differential Evolution AlgorithmFuxiao Ma0https://orcid.org/0000-0002-1901-0191Yunjie Ma1https://orcid.org/0000-0001-5788-7202Peili Li2https://orcid.org/0000-0001-8595-6471Weihua Shi3College of Electronic and Optical Engineering and College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing, ChinaCollege of Electronic and Optical Engineering and College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing, ChinaCollege of Electronic and Optical Engineering and College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing, ChinaCollege of Electronic and Optical Engineering and College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing, ChinaA Ge-doped dual-core dispersion compensation photonic crystal fiber (DC-DCPCF) is proposed. The small diameters of two layers’ air holes make DC-DCPCF form a dual-core structure, which is conducive to broadband dispersion compensation. Low Ge-doped silica as the only background material reduces the preparation difficulty and cost. It is inversely designed by using artificial neural network (ANN) combined with differential evolution algorithm (DE) to obtain target dispersion compensation. ANN replaces the finite element method to accomplish fast forward prediction of DC-DCPCF properties. DE solves the single solution problem of single or cascade network that makes it flexible and reproducible. The results demonstrate that the designed DC-DCPCF can not only compensate 45 and 25 times its length of Corning single-mode fiber 28 (SMF28) in S+C+L+U bands and E+S+C+L+U bands respectively, but also accurately compensate the residual dispersion with effective dispersion compensation being only +0.005∼+0.842ps/(nm·km) and −0.03∼+1.31ps/(nm·km), respectively. In addition, the kappa values of DCP-PCF are well matched with SMF28 in the broadband wavelength range. It takes only about 10 seconds to complete the inverse design of the target DC-DCPCF. It provides a design method for custom DC-DCPCF and an efficient inverse design solution for photonic automation in fiber optical communication systems.https://ieeexplore.ieee.org/document/10136190/Deep learningdifferential evolution algorithmdual-core dispersion compensation fiberenter keywords or phrases in alphabetical orderinverse designphotonic crystal fiber |
| spellingShingle | Fuxiao Ma Yunjie Ma Peili Li Weihua Shi Inverse Design of Broadband Dispersion Compensation Fiber Based on Deep Learning and Differential Evolution Algorithm IEEE Photonics Journal Deep learning differential evolution algorithm dual-core dispersion compensation fiber enter keywords or phrases in alphabetical order inverse design photonic crystal fiber |
| title | Inverse Design of Broadband Dispersion Compensation Fiber Based on Deep Learning and Differential Evolution Algorithm |
| title_full | Inverse Design of Broadband Dispersion Compensation Fiber Based on Deep Learning and Differential Evolution Algorithm |
| title_fullStr | Inverse Design of Broadband Dispersion Compensation Fiber Based on Deep Learning and Differential Evolution Algorithm |
| title_full_unstemmed | Inverse Design of Broadband Dispersion Compensation Fiber Based on Deep Learning and Differential Evolution Algorithm |
| title_short | Inverse Design of Broadband Dispersion Compensation Fiber Based on Deep Learning and Differential Evolution Algorithm |
| title_sort | inverse design of broadband dispersion compensation fiber based on deep learning and differential evolution algorithm |
| topic | Deep learning differential evolution algorithm dual-core dispersion compensation fiber enter keywords or phrases in alphabetical order inverse design photonic crystal fiber |
| url | https://ieeexplore.ieee.org/document/10136190/ |
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